Method and system for managing carbon dioxide supplies and supercritical turbines using machine learning

ABSTRACT

A method may include obtaining reservoir data for a geological region of interest. The method may further include obtaining turbine data regarding a supercritical carbon dioxide power (sCO2) turbine. The method may further include obtaining carbon emission data for a well coupled to the geological region of interest. The method may further include determining predicted production data and predicted carbon emission data using a machine-learning model, the reservoir data, the turbine data, and the carbon emission data. The method may further include transmitting a command to a control system based on the predicted production data and the predicted carbon emission data. The command may adjusts an amount of carbon dioxide that is distributed to an injection well and the sCO 2  turbine. The command achieves a predetermined production rate at the well and a predetermined carbon footprint.

BACKGROUND

Global climate change and air quality have become increasingly importantenvironmental concerns. Various contributors to global climate changeare greenhouse gases, which include carbon dioxide (CO₂), methane (CH₄),and nitrous oxide (N₂O). In particular, greenhouse gases may occurnaturally and as the result of oil and gas production. For example, CO₂emissions may result from well operations, such as exhaust from engines,turbines and fired heaters, gas flaring, well testing, and enhanced oilrecovery (EOR) operations. However, some attempts have been made in theoil and gas industry to reduce the amount of greenhouse gases. Inparticular, some operations recycle and reuse waste gases to conserveheat or minimize flaring. Likewise, improved leak detection practicescan also identify and fix gas leaks from tanks and other equipment.

SUMMARY

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

In general, in one aspect, embodiments relate to a method that includesobtaining, by a computer processor, reservoir data for a geologicalregion of interest. The method further includes obtaining, by thecomputer processor, turbine data regarding one or more supercriticalcarbon dioxide power (sCO₂) turbines. The method further includesobtaining, by the computer processor, carbon emission data for one ormore wells coupled to the geological region of interest. The methodfurther includes determining, by the computer processor, predictedproduction data and predicted carbon emission data using amachine-learning model, the reservoir data, the turbine data, and thecarbon emission data. The method further includes transmitting, by thecomputer processor, one or more commands to a control system based onthe predicted production data and the predicted carbon emission data.The one or more commands adjusts an amount of carbon dioxide that isdistributed to an injection well and the one or more sCO₂ turbines. Theone or more commands achieve a predetermined production rate at the oneor more wells and a predetermined carbon footprint. The predeterminedcarbon footprint corresponds to carbon emissions that are produced bythe one or more wells and the one or more sCO₂ turbines.

In general, in one aspect, embodiments relate to a method that includesa first control system coupled to an injection well, a second controlsystem coupled to a production well, and a third control system coupledto one or more supercritical carbon dioxide (sCO₂) turbines. The systemfurther includes a carbon dioxide manager that includes a computerprocessor, where the carbon dioxide manager is coupled to the firstcontrol system, the second control system, and the third control system.The carbon dioxide manager obtains reservoir data for a geologicalregion of interest coupled to the injection well and the productionwell. The carbon dioxide manager obtains turbine data regarding the oneor more supercritical sCO₂ turbines. The carbon dioxide manager obtainscarbon emission data for the injection well and the production well. Thecarbon dioxide manager determines predicted production data andpredicted carbon emission data using a machine-learning model, thereservoir data, the turbine data, and the carbon emission data. Thecarbon dioxide manager transmits one or more commands to the firstcontrol system, the second control system, or the third control systembased on the predicted production data and the predicted carbon emissiondata. The one or more commands achieve a predetermined production rateand a predetermined carbon footprint. The predetermined carbon footprintcorresponds to carbon emissions that are produced by the productionwell, the injection well, and the one or more sCO₂ turbines.

In some embodiments, a machine-learning model includes a neural networkthat includes at least one hidden layer, at least one neuron, and anactivation function, where the neural network determines a predictedproduction rate at a predetermined time for one or more wells. In someembodiments, a machine-learning model includes a neural network thatincludes at least one hidden layer, at least one neuron, and anactivation function, where the neural network determines a predeterminedamount of carbon dioxide emissions associated with providing electricpower to one or more wells to achieve a predetermined production rate.In some embodiments, a machine-learning model includes one or morenon-linear autoregressive neural network with an exogenous input (NARX)models. In some embodiments, a request is obtained from a user device todetermine carbon dioxide supplies within a carbon dioxide managementnetwork in response to a user input to the user device. In someembodiments, reservoir data include sensor data that describes gascontent in a reservoir based on one or more gas tracers. In someembodiments, predicted production data for a production well for apredetermined period of time is determined using a machine-learningmodel and based on historical injection data, where the predictedproduction data corresponds to a predetermined amount of oil, apredetermined amount of gas, and a predetermined amount of water thatare produced by a production well. In some embodiments, error data isdetermined based on a mismatch between predicted production data andacquired production data. A machine-learning model is updated using theerror data and a machine-learning algorithm, where the machine-learningalgorithm is a backward propagation algorithm. In some embodiments,predicted carbon emission data is determined using a machine-learningmodel for a predetermined period of time based on well data for theproduction well, historical injection data, and historicalelectric-power data, where the predicted carbon emission datacorresponds to electric-power requirements for the production well thatachieve carbon dioxide neutrality with a stimulation operation for thegeological region of interest. In some embodiments, predicted carbondioxide demand data is determined using a machine-learning model for ansCO₂ turbine for a predetermined period of time based on historicalelectric-power data. In some embodiments, one or more sCO₂ turbinesoperate using carbon dioxide in a fluid state of carbon dioxide (CO₂)that is held at or above a predetermined critical temperature and apredetermined critical pressure.

In light of the structure and functions described above, embodiments ofthe invention may include respective means adapted to carry out varioussteps and functions defined above in accordance with one or more aspectsand any one of the embodiments of one or more aspect described herein.

Other aspects and advantages of the claimed subject matter will beapparent from the following description and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

Specific embodiments of the disclosed technology will now be describedin detail with reference to the accompanying figures. Like elements inthe various figures are denoted by like reference numerals forconsistency.

FIGS. 1, 2, and 3 show systems in accordance with one or moreembodiments.

FIG. 4 shows a flowchart in accordance with one or more embodiments.

FIG. 5 shows a computer system in accordance with one or moreembodiments.

DETAILED DESCRIPTION

In the following detailed description of embodiments of the disclosure,numerous specific details are set forth in order to provide a morethorough understanding of the disclosure. However, it will be apparentto one of ordinary skill in the art that the disclosure may be practicedwithout these specific details. In other instances, well-known featureshave not been described in detail to avoid unnecessarily complicatingthe description.

Throughout the application, ordinal numbers (e.g., first, second, third,etc.) may be used as an adjective for an element (i.e., any noun in theapplication). The use of ordinal numbers is not to imply or create anyparticular ordering of the elements nor to limit any element to beingonly a single element unless expressly disclosed, such as using theterms “before”, “after”, “single”, and other such terminology. Rather,the use of ordinal numbers is to distinguish between the elements. Byway of an example, a first element is distinct from a second element,and the first element may encompass more than one element and succeed(or precede) the second element in an ordering of elements.

In general, embodiments of the disclosure include systems and methodsfor using artificial intelligence and machine learning to optimize fluidinjection (e.g., such as carbon dioxide or water) for enhancing areservoir while also optimizing carbon emissions. For example,sustainable reservoir management may be implemented while enhancingreservoir performance by recycling carbon emissions from productionwells for use in injection wells and supercritical carbon dioxide (CO₂)turbines. More specifically, supercritical CO₂ power turbines may allowfor the efficient utilization of CO₂ for electric power generationpurposes to avoid the consumption of CO₂ emitting alternatives.Accordingly, some embodiments use a machine-learning framework may beused for reservoir performance and turbine optimization as well asreducing the carbon footprint of various hydrocarbon productionfacilities. Using various data analyses and categorizations, themachine-learning framework may include a multi-factor nonlinearautoregressive neural network with exogeneous inputs (NARX) model thatis trained to forecast reservoir production and carbon dioxideconsumption by supercritical CO₂ power turbines. Likewise, one or moreNARX models may be used to manage carbon footprints based on productionoperations and stimulation operations. By predicting hydrocarbonproduction and carbon emission data, various well operations (e.g.,stimulation operations) may be adjusted to minimize an aggregate carbonfootprint from a reservoir while also maximizing reservoir performance.

Turning to FIG. 1 , FIG. 1 shows a schematic diagram in accordance withone or more embodiments. As shown in FIG. 1 , FIG. 1 illustrates a wellenvironment (100) that includes a hydrocarbon reservoir (“reservoir”)(102) located in a subsurface hydrocarbon-bearing formation (104) and awell system (106). The hydrocarbon-bearing formation (104) may include aporous or fractured rock formation that resides underground, beneath theearth’s surface (“surface”) (108). In the case of the well system (106)being a hydrocarbon well, the reservoir (102) may include a portion ofthe hydrocarbon-bearing formation (104). The hydrocarbon-bearingformation (104) and the reservoir (102) may include different layers ofrock having varying properties, such as varying degrees of permeability,porosity, and resistivity. In the case of the well system (106) beingoperated as a production well, the well system (106) may facilitate theextraction of hydrocarbons (or “production”) from the reservoir (102).

In some embodiments, the well system (106) includes a wellbore (120), awell sub-surface system (122), a well surface system (124), and a wellcontrol system (126). The control system (126) may control variousoperations of the well system (106), such as well production operations,well completion operations, well maintenance operations, and reservoirmonitoring, assessment and development operations. In some embodiments,the control system (126) includes a computer system that is the same asor similar to that of computer system (502) described below in FIG. 5and the accompanying description.

The wellbore (120) may include a bored hole that extends from thesurface (108) into a target zone of the hydrocarbon-bearing formation(104), such as the reservoir (102). An upper end of the wellbore (120),terminating at or near the surface (108), may be referred to as the“up-hole” end of the wellbore (120), and a lower end of the wellbore,terminating in the hydrocarbon-bearing formation (104), may be referredto as the “down-hole” end of the wellbore (120). The wellbore (120) mayfacilitate the circulation of drilling fluids during drillingoperations, the flow of hydrocarbon production (“production”) (121)(e.g., oil and gas) from the reservoir (102) to the surface (108) duringproduction operations, the injection of substances (e.g., water) intothe hydrocarbon-bearing formation (104) or the reservoir (102) duringinjection operations, or the communication of monitoring devices (e.g.,logging tools) into the hydrocarbon-bearing formation (104) or thereservoir (102) during monitoring operations (e.g., during in situlogging operations).

In some embodiments, during operation of the well system (106), thecontrol system (126) collects and records wellhead data (140) for thewell system (106). The wellhead data (140) may include, for example, arecord of measurements of wellhead pressure (P_(wh)) (e.g., includingflowing wellhead pressure), wellhead temperature (T_(wh)) (e.g.,including flowing wellhead temperature), wellhead production rate(Q_(wh)) over some or all of the life of the well (106), and water cutdata. In some embodiments, the measurements are recorded in real-time,and are available for review or use within seconds, minutes or hours ofthe condition being sensed (e.g., the measurements are available within1 hour of the condition being sensed). In such an embodiment, thewellhead data (140) may be referred to as “real-time” wellhead data(140). Real-time wellhead data (140) may enable an operator of the well(106) to assess a relatively current state of the well system (106), andmake real-time decisions regarding development of the well system (106)and the reservoir (102), such as on-demand adjustments in regulation ofproduction flow from the well.

In some embodiments, the well surface system (124) includes a wellhead(130). The wellhead (130) may include a rigid structure installed at the“up-hole” end of the wellbore (120), at or near where the wellbore (120)terminates at the Earth’s surface (108). The wellhead (130) may includestructures for supporting (or “hanging”) casing and production tubingextending into the wellbore (120). Production (121) may flow through thewellhead (130), after exiting the wellbore (120) and the wellsub-surface system (122), including, for example, the casing and theproduction tubing. In some embodiments, the well surface system (124)includes flow regulating devices that are operable to control the flowof substances into and out of the wellbore (120). For example, the wellsurface system (124) may include one or more production valves (132)that are operable to control the flow of production (134). For example,a production valve (132) may be fully opened to enable unrestricted flowof production (121) from the wellbore (120), the production valve (132)may be partially opened to partially restrict (or “throttle”) the flowof production (121) from the wellbore (120), and production valve (132)may be fully closed to fully restrict (or “block”) the flow ofproduction (121) from the wellbore (120), and through the well surfacesystem (124).

Keeping with FIG. 1 , in some embodiments, the well surface system (124)includes a surface sensing system (134). The surface sensing system(134) may include sensors for sensing characteristics of substances,including production (121), passing through or otherwise located in thewell surface system (124). The characteristics may include, for example,pressure, temperature and flow rate of production (121) flowing throughthe wellhead (130), or other conduits of the well surface system (124),after exiting the wellbore (120).

In some embodiments, the surface sensing system (134) includes a surfacepressure sensor (136) operable to sense the pressure of production (151)flowing through the well surface system (124), after it exits thewellbore (120). The surface pressure sensor (136) may include, forexample, a wellhead pressure sensor that senses a pressure of production(121) flowing through or otherwise located in the wellhead (130). Insome embodiments, the surface sensing system (134) includes a surfacetemperature sensor (138) operable to sense the temperature of production(151) flowing through the well surface system (124), after it exits thewellbore (120). The surface temperature sensor (138) may include, forexample, a wellhead temperature sensor that senses a temperature ofproduction (121) flowing through or otherwise located in the wellhead(130), referred to as “wellhead temperature” (T_(wh)). In someembodiments, the surface sensing system (134) includes a flow ratesensor (139) operable to sense the flow rate of production (151) flowingthrough the well surface system (124), after it exits the wellbore(120). The flow rate sensor (139) may include hardware that senses aflow rate of production (121) (Q_(wh)) passing through the wellhead(130).

In some embodiments, the well system (106) includes a reservoirsimulator (160). For example, the reservoir simulator (160) may includehardware and/or software with functionality for generating one or morereservoir models regarding the hydrocarbon-bearing formation (104)and/or performing one or more reservoir simulations. For example, thereservoir simulator (160) may store well logs and data regarding coresamples for performing simulations. A reservoir simulator may furtheranalyze the well log data, the core sample data, seismic data, and/orother types of data to generate and/or update the one or more reservoirmodels. While the reservoir simulator (160) is shown at a well site,embodiments are contemplated where reservoir simulators are located awayfrom well sites. In some embodiments, the reservoir simulator (160) mayinclude a computer system that is similar to the computer system (502)described below with regard to FIG. 5 and the accompanying description.

Turning to FIG. 2 , FIG. 2 shows a schematic diagram in accordance withone or more embodiments. As illustrated in FIG. 2 , FIG. 2 shows ageological region (200) that may include one or more reservoir regions(e.g., reservoir region (230)) with various production wells (e.g.,production well A (211), production well (212)). For example, aproduction well may be similar to the well system (106) described abovein FIG. 1 and the accompanying description. Likewise, a reservoir regionmay also include one or more injection wells (e.g., injection well C(216)) that include functionality for enhancing production by one ormore neighboring production wells. As shown in FIG. 2 , wells may bedisposed in the reservoir region (230) above various subsurface layers(e.g., subsurface layer A (241), subsurface layer B (242)), which mayinclude hydrocarbon deposits. In particular, production data and/orinjection data may exist for a particular well, where production datamay include data that describes production or production operations at awell, such as wellhead data (140) described in FIG. 1 and theaccompanying description.

In some embodiments, production wells and/or injection wells are used inone or more stimulation operations. For example, one type of stimulationoperation is a water-alternating-gas (WAG) operation. A WAG operationmay be a cyclic process of injecting water followed by gas. Using a WAGinjection, macroscopic or microscopic sweep efficiency may be improvedfor a reservoir, e.g., by maintaining nearly initial high pressure, slowdown any gas breakthroughs, and reduce oil viscosity. Likewise, WAGinjections may also decrease residual oil saturation resulting fromthree phase flows and effects associated with relative permeabilityhysteresis. Thus, some stimulation operations may produce gas flooding,which is a type of enhanced oil recovery (EOR) method for increasingrecovery of light to moderate oil reservoirs. In some stimulationoperations, water may be injected during the initial phase of theoperation and followed by a gas (e.g., carbon dioxide) because water mayhave a higher mobility ratio than the injected gas, thereby preventingbreakthroughs in the reservoir. Injected gas may be a mixture ofhydrocarbon gas or nonhydrocarbon gases. With hydrocarbon gases, the gasmixture may include methane, ethane, and propane for achieving amiscible or immiscible gas-oil system in the reservoir. Withnonhydrocarbon gases, the gas mixture may include carbon dioxide (CO₂),nitrogen (N₂), and some exotic gases that displace fluid in thereservoir. Likewise, gas may also be injected directly into a reservoir,e.g., into the gas cap, to compensate for the reservoir’s pressuredecline.

Furthermore, a stimulation injection during a stimulation operation maycorrespond to various injection parameters, such as bank size, cycletime, and a predetermined water-gas ratio (also called a “WAG ratio”).Bank size may refer to a size of sequential banks of fluids (e.g., oil,CO₂ and water) formed in the reservoir rock in response to a stimulationoperation that migrate from the injection to the production wells. Forillustration, a WAG ratio of 1:1 may result in a high oil production forone or more production wells, such as production wells coupled to amiscible reservoir. Based on some reservoir parameters such as oilcomposition, gas flooding can be carried out in miscible or immiscibleconditions. Moreover, different types of stimulation operations may usedifferent stimulation parameters. Examples of different stimulationoperations may include: (1) continuous gas injections; (2) WAGinjections; (3) simultaneous water-alternating-gas (SWAG) injections;and (4) tapered WAG injections. Different strategies have been developedby the petroleum industry to cope with these conditions.

Turning to FIG. 3 , FIG. 3 shows a schematic diagram in accordance withone or more embodiments. As shown in FIG. 3 , a carbon dioxidemanagement network (e.g., carbon dioxide management network A (300)) mayinclude various well and power-generation facilities (e.g.,supercritical CO₂ turbine A (310), production well B (320), injectionwell C (340)), various user devices (e.g., user device M (330)), andvarious network elements (not shown). The well facilities andpower-generation facilities may include various control systems (e.g.,control system A (317), control system B (327), control system C (347)),sensor devices (e.g., sensors B (325), sensors C (345)), and otherequipment. The carbon dioxide management network A (300) may be similarto network (530) described below in FIG. 5 and the accompanyingdescription. User devices may include personal computers, handheldcomputer devices such as a smartphone or personal digital assistant, ora human machine interface (HMI). Well equipment and power-generationequipment may include storage tanks, heat exchangers, accumulators,boilers, pumps, inlet separators, coolers, evaporators, instruments,gauges, control switches, valves, emergency stop controls, pressurerelief equipment, flaring equipment, smoke detectors, toxic gasdetectors, thermal detectors, combustible gas detectors, electric powergenerators, turbines, exhaust fans, light panels, fume scrubbers, safetyshowers, and plant equipment.

In some embodiments, a carbon dioxide manager includes hardware and/orsoftware with functionality for managing one or more carbon dioxidesupplies (e.g., carbon dioxide supply A (321), carbon dioxide supply B(322)) within a carbon dioxide management network. For example, thecarbon dioxide manager may control carbon emissions relating toelectric-power production or hydrocarbon production using data obtainedover the carbon dioxide management network. Data types may includereservoir data (e.g., reservoir data X (362)), such as well data (e.g.,well data B (392), well data X (361)) and sensor data (e.g., datacollected from sensors B (325) or sensors C (345)), electric-power datafor operating well equipment (e.g., electric-power data X (367)), andproduction data (e.g., production data X (365)), turbine data (e.g.,turbine data A (393)), stimulation parameters (e.g., stimulationparameters X (366)), and user data (e.g., user data (333)). Likewise,the carbon dioxide manager may also control carbon dioxide and other gasinjections into a reservoir during stimulation operations. Inparticular, the carbon dioxide manager may determine predicted carbonfootprint data, predicted carbon emission data, predicted productiondata (e.g., in response to a stimulation operation), and/or predictedelectric-power data to optimizing hydrocarbon production whileminimizing a carbon footprint of components in the carbon dioxidemanagement network.

In some embodiments, for example, a carbon dioxide manager may includeone or more machine-learning models (e.g., machine-learning models Y(364)) to determine predicted data. In particular, a carbon dioxidemanager may adjust parameters of one or more stimulation operations,production operations, and electric-power generation operations usingmachine learning. Different types of machine-learning models may betrained, such as convolutional neural networks, deep neural networks,recurrent neural networks, support vector machines, decision trees,inductive learning models, deductive learning models, supervisedlearning models, unsupervised learning models, reinforcement learningmodels, etc. In some embodiments, two or more different types ofmachine-learning models are integrated into a single machine-learningarchitecture, e.g., a machine-learning model may include a supportvector machine and multiple neural networks. In some embodiments, thecarbon dioxide manager may generate augmented data or synthetic data toproduce a large amount of interpreted data for training a particularmodel.

In some embodiments, various types of machine learning algorithms (e.g.,machine-learning algorithms X (363)) may be used to train the model,such as a backpropagation algorithm. In a backpropagation algorithm,gradients are computed for each hidden layer of a neural network inreverse from the layer closest to the output layer proceeding to thelayer closest to the input layer. As such, a gradient may be calculatedusing the transpose of the weights of a respective hidden layer based onan error function (also called a “loss function”). The error functionmay be based on various criteria, such as mean squared error function, asimilarity function, etc., where the error function may be used as afeedback mechanism for tuning weights in the machine-learning model(e.g., one of machine-learning models Y (364)).

With respect to neural networks, for example, a neural network mayinclude one or more hidden layers, where a hidden layer includes one ormore neurons. A neuron may be a modelling node or object that is looselypatterned on a neuron of the human brain. In particular, a neuron maycombine data inputs with a set of coefficients, i.e., a set of networkweights for adjusting the data inputs. These network weights may amplifyor reduce the value of a particular data input, thereby assigning anamount of significance to various data inputs for a task being modeled.Through machine learning, a neural network may determine which datainputs should receive greater priority in determining one or morespecified outputs of the neural network. Likewise, these weighted datainputs may be summed such that this sum is communicated through aneuron’s activation function to other hidden layers within the neuralnetwork. As such, the activation function may determine whether and towhat extent an output of a neuron progresses to other neurons where theoutput may be weighted again for use as an input to the next hiddenlayer.

Turning to recurrent neural networks, a recurrent neural network (RNN)may perform a particular task repeatedly for multiple data elements inan input sequence (e.g., a sequence of electric-power data, productiondata, reservoir data such as wellhead data or sensor data), with theoutput of the recurrent neural network being dependent on pastcomputations (e.g., future production rates at a given production wellmay be in response to past stimulation operations at one or moreinjection wells and past production from the respective reservoir). Assuch, a recurrent neural network may operate with a memory or hiddencell state, which provides information for use by the current cellcomputation with respect to the current data input. For example, arecurrent neural network may resemble a chain-like structure of RNNcells, where different types of recurrent neural networks may havedifferent types of repeating RNN cells. Likewise, the input sequence maybe time-series data, where hidden cell states may have different valuesat different time steps during a prediction or training operation. Forexample, where a deep neural network may use different parameters ateach hidden layer, a recurrent neural network may have common parametersin an RNN cell, which may be performed across multiple time steps. Totrain a recurrent neural network, a supervised learning algorithm suchas a backpropagation algorithm may also be used. In some embodiments,the backpropagation algorithm is a backpropagation through time (BPTT)algorithm. Likewise, a BPTT algorithm may determine gradients to updatevarious hidden layers and neurons within a recurrent neural network in asimilar manner as used to train various deep neural networks. In someembodiments, a recurrent neural network is trained using a reinforcementlearning algorithm such as a deep reinforcement learning algorithm. Formore information on reinforcement learning algorithms, see thediscussion below.

Embodiments are contemplated with different types of RNNs. For example,classic RNNs, long short-term memory (LSTM) networks, a gated recurrentunit (GRU), a stacked LSTM that includes multiple hidden LSTM layers(i.e., each LSTM layer includes multiple RNN cells), recurrent neuralnetworks with attention (i.e., the machine-learning model may focusattention on specific elements in an input sequence), bidirectionalrecurrent neural networks (e.g., a machine-learning model that may betrained in both time directions simultaneously, with separate hiddenlayers, such as forward layers and backward layers), as well asmultidimensional LSTM networks, graph recurrent neural networks, gridrecurrent neural networks, etc. With regard to LSTM networks, an LSTMcell may include various output lines that carry vectors of information,e.g., from the output of one LSTM cell to the input of another LSTMcell. Thus, an LSTM cell may include multiple hidden layers as well asvarious pointwise operation units that perform computations such asvector addition.

In some embodiments, a carbon dioxide manager uses one or more nonlinearautoregressive network with exogenous inputs (NARX) models to predictdata. For example, a NARX model may be a recurrent dynamic network withfeedback connections enclosing several hidden layers of themachine-learning model. The NARX model may be based on a linear ARXmodel as used in time-series modeling. As such, autoregressive models,such as the NARX model, may obtain one or more time series signals andvarious exogenous features as inputs, respectively. For example, a NARXmodel may have only one hidden layer with a delay of one in the modelthat may allow the NARX model to perform a one-step-ahead prediction.NARX models may be useful for identification of nonlinear systems usinga nonlinearity estimator and one or more model regressors. Whilenonlinearity estimators may act on the designed regressors to predictthe model outputs, the model regressors may provide the delayed outputsand delayed inputs.

Keeping with FIG. 3 , in some embodiments, a carbon dioxide managementnetwork includes one or more supercritical CO₂ turbines (e.g.,supercritical CO₂ turbine A (310)). More specifically, supercriticalcarbon dioxide (sCO₂) may correspond to a fluid state of carbon dioxide,where the supercritical carbon dioxide may be held at or above itscritical temperature and critical pressure. While carbon dioxide usuallybehaves as a gas in air at standard temperature and pressure (STP),carbon dioxide may adopt properties between a gas and a liquid aspressure and temperature increase. Thus, at its supercritical state,carbon dioxide may be nearly twice as dense as steam, thereby allowingthe size of turbine system components to be considerably reduced. Assuch, supercritical CO₂ turbines may operate using supercritical carbondioxide instead of steam, thereby operating with a smaller carbonfootprint than a steam-powered turbine. Examples of supercritical CO₂turbines include simple closed-loop Brayton cycle turbines, recuperatedclosed-loop Brayton cycle turbines, recuperated recompressionclosed-loop Brayton cycle turbines, semi-closed direct oxyfuel Braytoncycle turbines, as well as other types of turbines.

Furthermore, a carbon dioxide management network includes varioussensors coupled to one or more production wells and one or moreinjection wells. In some embodiments, for example, a reservoir ismonitored using point sensors, quasi-distributed sensor networks, and/ordistributed sensor networks. Point sensors may monitor a reservoir atdiscrete points, while a quasi-distributed sensor network may monitor areservoir at multiple discrete points situated. Likewise, distributedsensing may be used to monitor reservoir parameters continuously alongan entire reservoir section, such as along an entire optical fiber inthe context of optical fiber sensors (OFS). Using electromagneticsensors such as OFSes, sensor data may be transmitted using one or moreoptical channels. In some embodiments, a carbon dioxide managementnetwork includes a distributed chemical sensing (DCS) network in one ormore wells. For example, optical fiber-based chemical sensors may beenabled through functional chemical sensing coatings (e.g., disposed onthe fiber core or cladding), such as metallic films, oxides, and/orpolymers.

In some embodiments, a carbon dioxide manager obtains sensor data basedon one or more gas tracers applied to a reservoir. For example, gastracers may be used to monitor and detect carbon dioxide displacement inthe subsurface of a geological region. In particular, gas tracers maydetect carbon dioxide leakages (e.g., using gas tracers as afingerprinting tool) to the upper layers in geological regions, thusproviding sensor data that describe carbon emissions and other gasemissions from gases injected into a reservoir. Examples of gas tracersmay include perfluorocarbons (PFCs), sulfur hexafluoride (SF₆), andvarious noble gases such as He, Ne, Ar, Kr, and Xe. Using variousisotopic gas ratios, gas tracers may detect a CO₂ anomaly and identifyits origin, since gas tracers may produce unique signatures within areservoir.

In some embodiments, gas tracers are included in an injected CO₂ streamduring one or more stimulation operations. In other words, injectedcarbon dioxide may become distinguishable from shallow fluids (e.g.,subsea gas seeps) due to its inheritance of the radiogenic signaturefrom a corresponding gas tracer (e.g., high Helium content may produce aunique signature that differentiates the gas mixture fromnaturally-occurring carbon dioxide). Likewise, the injected CO₂ streammay also be used to verify the presence of injected CO₂, monitor CO₂leakage and measure reservoir behavior of CO₂ under injection (e.g.,saturation, fluid mixing, etc.) In other words, injected carbon dioxidemay become distinguishable from shallow fluids (e.g., subsea gas seeps)due to its inheritance of the radiogenic signature from thecorresponding gas tracer. Furthermore, gas tracer data may be used inboth production prediction and carbon emission prediction throughanalyses of reservoir fluids, shallow groundwater, deep soil, surfaceemissions, and formation gases. In some embodiments, for example, a gastracer is extracted through a thermal desorption system and subsequentlyanalyzed in a gas chromatograph with an electron capture detector (GC-ECD).

Turning to control systems, control systems may include a programmablelogic controller (PLC), a distributed control system (DCS), asupervisory control and data acquisition (SCADA), and/or a remoteterminal unit (RTU). For example, a programmable logic controller maycontrol valve states, fluid levels, pipe pressures, warning alarms,and/or pressure releases throughout a well facility or power-generationfacility. In particular, a programmable logic controller may be aruggedized computer system with functionality to withstand vibrations,extreme temperatures, wet conditions, and/or dusty conditions, forexample, around a refinery. A distributed control system may be acomputer system for managing various processes at various facilitiesusing multiple control loops. As such, a distributed control system mayinclude various autonomous controllers (such as remote terminal units)positioned at different locations throughout the facility to manageoperations and monitor processes. Likewise, a distributed control systemmay include no single centralized computer for managing control loopsand other operations. On the other hand, a SCADA system may include acontrol system that includes functionality for enabling monitoring andissuing of process commands through local control at a facility as wellas remote control outside the facility. With respect to an RTU, an RTUmay include hardware and/or software, such as a microprocessor, thatconnects sensors and/or actuators using network connections to performvarious processes in the automation system. Likewise, a control systemmay be coupled to one or more well devices or electric-power generationdevices.

In some embodiments, a user device (e.g., user device M (330)) maycommunicate with a carbon dioxide manager to manage carbon dioxideemissions, carbon footprints, electric-power generation at one or morepower turbines, and/or well production based on one or more userselections (e.g., user selections N (331)). For example, a user mayinteract with a user interface (e.g., user interface O (332)) to changethresholds for different carbon dioxide levels or production levels,e.g., for carbon footprint and production optimizations. Through userselections or automation, the carbon dioxide manager may provide variousreports for different well facilities, power-generation facilities, andother information in a graphical user interface regarding predictedproduction data, predicted electric-power data, and carbon emissiondata.

In some embodiments, the carbon dioxide manager includes functionalityfor transmitting commands (e.g., command Y (395) is transmitted to acontrol system in injection well C (340)) to one or more user devicesand/or control systems to implement a particular production operation,stimulation operation, and/or power-generation operation. For example,the carbon dioxide manager X (360) may transmit a network message over amachine-to-machine protocol to the control system C (347). A command maybe transmitted based on a user input or automatically based on changesin production conditions, e.g., after analyzing new reservoir data,electric-power data, and carbon emission data.

While FIGS. 1, 2, and 3 shows various configurations of components,other configurations may be used without departing from the scope of thedisclosure. For example, various components in FIGS. 1, 2, and 3 may becombined to create a single component. As another example, thefunctionality performed by a single component may be performed by two ormore components.

Turning to FIG. 4 , FIG. 4 shows a flowchart in accordance with one ormore embodiments. Specifically, FIG. 4 describes a general method formanaging stimulation operations and supercritical carbon dioxideturbines based on carbon dioxide emissions and/or optimizing hydrocarbonproduction. One or more blocks in FIG. 4 may be performed by one or morecomponents (e.g., carbon dioxide manager X (360)) as described in FIGS.1, 2, and 3 . While the various blocks in FIG. 4 are presented anddescribed sequentially, one of ordinary skill in the art will appreciatethat some or all of the blocks may be executed in different orders, maybe combined or omitted, and some or all of the blocks may be executed inparallel. Furthermore, the blocks may be performed actively orpassively.

In Block 400, a request is obtained to determine a predeterminedproduction rate and/or a carbon footprint for a geological region ofinterest in accordance with one or more embodiments. For example, a usermay transmit a request in response to a user input provided to a userdevice. The request may be a network message transmitted between a userdevice and a carbon dioxide manager that identifies desired productioncriteria for production wells or desired levels of carbon emissions forone or more wells or well equipment.

In Block 405, reservoir data are obtained for a geological region ofinterest in accordance with one or more embodiments. For example,reservoir heterogeneity and the adverse effects of CO₂ viscosity must becontended with to optimize oil recovery. For example, the reservoir datamay include wellhead data, well logs, seismic data, and sensor data. Inparticular, reservoir data may include a combination of time lapsed gastracers, resistivity data, chargeability well logs and deep reservoirimaging technologies, such as electromagnetic surveys. In particular,the reservoir data may correspond to changes of volumetric saturationdeep into a geological region of interest.

In some embodiments, a carbon dioxide manager may obtain sensor datafrom inline chromatography-based sensors, e.g., for real-time analysisof tracers, such as gas tracers. In particular, various tracers may beused to qualitatively or quantitatively gauge downhole plume,containment, breakthrough, and reservoir cap rock and top seal integritymonitoring (e.g., in regard to CO₂ and other gases) through thereservoir. In some embodiments, tracer surveys may performed on ageological region of interest through inter-well tests or single welltests. Likewise, tracers may also be used for flow profiling inhorizontal well. In flow profiling, different solid gas/water-solubletracer chips may be disposed along the length of a tubing. Upongas/water breakthrough, fluid samples may be collected at the surface.In some embodiments, analysis of sensor data from gas tracers may show aqualitative gas-water-inflow profile.

In Block 410, turbine data are obtained regarding one or moresupercritical carbon dioxide turbines in accordance with one or moreembodiments. For example, turbine data may include operation hours ofturbines, electricity utilization, and other information relating tosupercritical CO₂ turbines, such as related carbon emissions. Likewise,the turbine data may also include power requirements for the operationsand the supercritical CO₂ turbine power generation capacity.

In Block 415, production data are obtained regarding one or more wellscoupled to a geological region of interest in accordance with one ormore embodiments. Production data may include well production data,operation hours of turbines, electricity utilization data, and otherproduction data. For the data analysis, a carbon dioxide manager mayremove any outlier in the data, and checks for data consistency. In someembodiments, human expert data is used to the enhance the initial dataprocessing and consistency. The initial processed data may thus becategorized different factors or parameters impact on the carbonfootprint, where an adapted generalized discriminant analysis may beutilized to determine sensitivity and correlation.

In Block 420, carbon emission data are obtained regarding one or morewells coupled to a geological region of interest in accordance with oneor more embodiments.

In Block 425, predicted production data are determined for one or morewells coupled to a geological region of interest using amachine-learning model, reservoir data and production data in accordancewith one or more embodiments.

In Block 430, predicted carbon emission data are determined for one ormore wells using a machine-learning model, reservoir data, carbonemission data, and turbine data in accordance with one or moreembodiments. In some embodiments, the machine-learning model is a NARXmodel. For example, the NARX model may include hundreds of fullyconnected layers with one or more activation functions, such as asigmoid activation function. Input data may be weighted according to aninitial categorization and various input features may be evaluated basedon their impact on the carbon footprint. The weighting may be alsoadapted according to various user requirements, and depends on thereservoir conditions.

In Block 440, predicted production data and/or predicted carbon emissiondata are presented in a graphical user interface in accordance with oneor more embodiments.

In Block 450, a determination is made whether predicted production dataor predicted carbon emission data satisfy a predetermined criterion inaccordance with one or more embodiments. For example, the predeterminedcriterion may correspond to a predetermined carbon emission levels(i.e., to achieve a predetermined carbon footprint) or predeterminedproduction levels. For example, the predetermined criterion may beselected by a user using a user device, or automatically based on acarbon dioxide manager, e.g., according to one or more optimizationalgorithms. If current production and carbon emissions satisfy thepredetermined criterion, the process may proceed to Block 405 forcontinued monitoring. If the predicted production data or the predictedcarbon emission data fail to satisfy a predetermined criterion, theprocess may proceed to Block 460.

In Block 460, carbon dioxide supplies are determined for one or moreinjection wells and one or more supercritical carbon dioxide turbinesusing predicted production data, predicted carbon emission data, and apredetermined criterion in accordance with one or more embodiments.

In Block 470, one or more commands are transmitted to one or morecontrol systems that adjust one or more carbon dioxide supplies inaccordance with one or more embodiments. Using smart/intelligentcompletions for wells, a carbon dioxide manager may redistributedownhole flow thru various inflow control devices (ICDs), for example,to have an even flow with shut/restrict the zones with the highest waterrate. Thus, stimulation operations may be optimized using collectedsensor data. Likewise, the carbon dioxide manager may also optimize theamount of CO₂ needed for generating electric power by varioussupercritical CO₂ turbines.

Embodiments may be implemented on a computer system. FIG. 5 is a blockdiagram of a computer system (502) used to provide computationalfunctionalities associated with described algorithms, methods,functions, processes, flows, and procedures as described in the instantdisclosure, according to an implementation. The illustrated computer(502) is intended to encompass any computing device such as a highperformance computing (HPC) device, server, desktop computer,laptop/notebook computer, wireless data port, smart phone, personal dataassistant (PDA), tablet computing device, one or more computerprocessors within these devices, or any other suitable processingdevice, including both physical or virtual instances (or both) of thecomputing device. Additionally, the computer (502) may include acomputer that includes an input device, such as a keypad, keyboard,touch screen, or other device that can accept user information, and anoutput device that conveys information associated with the operation ofthe computer (502), including digital data, visual, or audio information(or a combination of information), or a GUI.

The computer (502) can serve in a role as a client, network component, aserver, a database or other persistency, or any other component (or acombination of roles) of a computer system for performing the subjectmatter described in the instant disclosure. The illustrated computer(502) is communicably coupled with a network (530). In someimplementations, one or more components of the computer (502) may beconfigured to operate within environments, includingcloud-computing-based, local, global, or other environment (or acombination of environments).

At a high level, the computer (502) is an electronic computing deviceoperable to receive, transmit, process, store, or manage data andinformation associated with the described subject matter. According tosome implementations, the computer (502) may also include or becommunicably coupled with an application server, e-mail server, webserver, caching server, streaming data server, business intelligence(BI) server, or other server (or a combination of servers).

The computer (502) can receive requests over network (530) from a clientapplication (for example, executing on another computer (502)) andresponding to the received requests by processing the said requests inan appropriate software application. In addition, requests may also besent to the computer (502) from internal users (for example, from acommand console or by other appropriate access method), external orthird-parties, other automated applications, as well as any otherappropriate entities, individuals, systems, or computers.

Each of the components of the computer (502) can communicate using asystem bus (503). In some implementations, any or all of the componentsof the computer (502), both hardware or software (or a combination ofhardware and software), may interface with each other or the interface(504) (or a combination of both) over the system bus (503) using anapplication programming interface (API) (512) or a service layer (513)(or a combination of the API (512) and service layer (513). The API(512) may include specifications for routines, data structures, andobject classes. The API (512) may be either computer-languageindependent or dependent and refer to a complete interface, a singlefunction, or even a set of APIs. The service layer (513) providessoftware services to the computer (502) or other components (whether ornot illustrated) that are communicably coupled to the computer (502).The functionality of the computer (502) may be accessible for allservice consumers using this service layer. Software services, such asthose provided by the service layer (513), provide reusable, definedbusiness functionalities through a defined interface. For example, theinterface may be software written in JAVA, C++, or other suitablelanguage providing data in extensible markup language (XML) format orother suitable format. While illustrated as an integrated component ofthe computer (502), alternative implementations may illustrate the API(512) or the service layer (513) as stand-alone components in relationto other components of the computer (502) or other components (whetheror not illustrated) that are communicably coupled to the computer (502).Moreover, any or all parts of the API (512) or the service layer (513)may be implemented as child or sub-modules of another software module,enterprise application, or hardware module without departing from thescope of this disclosure.

The computer (502) includes an interface (504). Although illustrated asa single interface (504) in FIG. 5 , two or more interfaces (504) may beused according to particular needs, desires, or particularimplementations of the computer (502). The interface (504) is used bythe computer (502) for communicating with other systems in a distributedenvironment that are connected to the network (530). Generally, theinterface (504 includes logic encoded in software or hardware (or acombination of software and hardware) and operable to communicate withthe network (530). More specifically, the interface (504) may includesoftware supporting one or more communication protocols associated withcommunications such that the network (530) or interface’s hardware isoperable to communicate physical signals within and outside of theillustrated computer (502).

The computer (502) includes at least one computer processor (505).Although illustrated as a single processor (505) in FIG. 5 , two or morecomputer processors may be used according to particular needs, desires,or particular implementations of the computer (502). Generally, thecomputer processor (505) executes instructions and manipulates data toperform the operations of the computer (502) and any algorithms,methods, functions, processes, flows, and procedures as described in theinstant disclosure.

The computer (502) also includes a memory (506) that holds data for thecomputer (502) or other components (or a combination of both) that canbe connected to the network (530). For example, memory (506) can be adatabase storing data consistent with this disclosure. Althoughillustrated as a single memory (506) in FIG. 5 , two or more memoriesmay be used according to particular needs, desires, or particularimplementations of the computer (502) and the described functionality.While memory (506) is illustrated as an integral component of thecomputer (502), in alternative implementations, memory (506) can beexternal to the computer (502).

The application (507) is an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer (502), particularly with respect tofunctionality described in this disclosure. For example, application(507) can serve as one or more components, modules, applications, etc.Further, although illustrated as a single application (507), theapplication (507) may be implemented as multiple applications (507) onthe computer (502). In addition, although illustrated as integral to thecomputer (502), in alternative implementations, the application (507)can be external to the computer (502).

There may be any number of computers (502) associated with, or externalto, a computer system containing computer (502), each computer (502)communicating over network (530). Further, the term “client,” “user,”and other appropriate terminology may be used interchangeably asappropriate without departing from the scope of this disclosure.Moreover, this disclosure contemplates that many users may use onecomputer (502), or that one user may use multiple computers (502).

In some embodiments, the computer (502) is implemented as part of acloud computing system. For example, a cloud computing system mayinclude one or more remote servers along with various other cloudcomponents, such as cloud storage units and edge servers. In particular,a cloud computing system may perform one or more computing operationswithout direct active management by a user device or local computersystem. As such, a cloud computing system may have different functionsdistributed over multiple locations from a central server, which may beperformed using one or more Internet connections. More specifically,cloud computing system may operate according to one or more servicemodels, such as infrastructure as a service (IaaS), platform as aservice (PaaS), software as a service (SaaS), mobile “backend” as aservice (MBaaS), serverless computing, and/or function as a service(FaaS).

Although only a few example embodiments have been described in detailabove, those skilled in the art will readily appreciate that manymodifications are possible in the example embodiments without materiallydeparting from this invention. Accordingly, all such modifications areintended to be included within the scope of this disclosure as definedin the following claims. In the claims, any means-plus-function clausesare intended to cover the structures described herein as performing therecited function(s) and equivalents of those structures. Similarly, anystep-plus-function clauses in the claims are intended to cover the actsdescribed here as performing the recited function(s) and equivalents ofthose acts. It is the express intention of the applicant not to invoke35 U.S.C. § 112(f) for any limitations of any of the claims herein,except for those in which the claim expressly uses the words “means for”or “step for” together with an associated function.

What is claimed:
 1. A method, comprising: obtaining, by a computerprocessor, reservoir data for a geological region of interest;obtaining, by the computer processor, turbine data regarding one or moresupercritical carbon dioxide power (sCO₂) turbines; obtaining, by thecomputer processor, carbon emission data for one or more wells coupledto the geological region of interest; determining, by the computerprocessor, predicted production data and predicted carbon emission datausing a first machine-learning model, the reservoir data, the turbinedata, and the carbon emission data; and transmitting, by the computerprocessor, one or more commands to a control system based on thepredicted production data and the predicted carbon emission data,wherein the one or more commands adjusts an amount of carbon dioxidethat is distributed to an injection well and the one or more sCO₂turbines, wherein the one or more commands achieve a predeterminedproduction rate at the one or more wells and a predetermined carbonfootprint, and wherein the predetermined carbon footprint corresponds tocarbon emissions that are produced by the one or more wells and the oneor more sCO₂ turbines.
 2. The method of claim 1, wherein the firstmachine-learning model comprises a neural network comprising at leastone hidden layer, at least one neuron, and an activation function, andwherein the neural network determines a predicted production rate at apredetermined time for the one or more wells.
 3. The method of claim 1,wherein the first machine-learning model comprises a neural networkcomprising at least one hidden layer, at least one neuron, and anactivation function, and wherein the neural network determines apredetermined amount of carbon dioxide emissions associated withproviding electric power to the one or more wells to achieve apredetermined production rate.
 4. The method of claim 1, wherein thefirst machine-learning model comprises one or more non-linearautoregressive neural network with an exogenous input (NARX) models. 5.The method of claim 1, further comprising: obtaining, by the computerprocessor and from a user device, a request to determine carbon dioxidesupplies within a carbon dioxide management network in response to auser input to the user device.
 6. The method of claim 1, wherein thereservoir data comprise sensor data that describes gas content in thereservoir based on one or more gas tracers.
 7. The method of claim 1,further comprising: determining, using a second machine-learning model,predicted production data for a production well for a predeterminedperiod of time based on historical injection data, wherein the predictedproduction data corresponds to a predetermined amount of oil, apredetermined amount of gas, and a predetermined amount of water thatare produced by the production well.
 8. The method of claim 1, furthercomprising: determining error data based on a mismatch between thepredicted production data and acquired production data; and updating asecond machine-learning model using the error data and amachine-learning algorithm, wherein the machine-learning algorithm is abackward propagation algorithm.
 9. The method of claim 1, furthercomprising: determining, using a second machine-learning model, thepredicted carbon emission data for a predetermined period of time basedon well data for a production well, historical injection data, andhistorical electric-power data, wherein the predicted carbon emissiondata corresponds to electric power requirements for the production wellthat achieve carbon dioxide neutrality with a stimulation operation forthe geological region of interest.
 10. The method of claim 1, furthercomprising: determining, using a second machine-learning model,predicted carbon dioxide demand data for an sCO₂ turbine for apredetermined period of time based on historical electric-power data.11. The method of claim 1, wherein the one or more sCO₂ turbines operateusing carbon dioxide in a fluid state of carbon dioxide (CO₂) that isheld at or above a predetermined critical temperature and apredetermined critical pressure.
 12. A system, comprising: a firstcontrol system coupled to an injection well; a second control systemcoupled to a production well; a third control system coupled to one ormore supercritical carbon dioxide (sCO₂) turbines; and a carbon dioxidemanager comprising a computer processor, wherein the carbon dioxidemanager is coupled to the first control system, the second controlsystem, and the third control system, the carbon dioxide manager beingconfigured to: obtain reservoir data for a geological region of interestcoupled to the injection well and the production well; obtain turbinedata regarding the one or more supercritical sCO₂ turbines; obtaincarbon emission data for the injection well and the production well;determine predicted production data and predicted carbon emission datausing a machine-learning model, the reservoir data, the turbine data,and the carbon emission data; and transmit one or more commands to thefirst control system, the second control system, or the third controlsystem based on the predicted production data and the predicted carbonemission data, wherein the one or more commands achieve a predeterminedproduction rate and a predetermined carbon footprint, and wherein thepredetermined carbon footprint corresponds to carbon emissions that areproduced by the production well, the injection well, and the one or moresCO₂ turbines.
 13. The system of claim 12, wherein the machine-learningmodel is a recurrent neural network model, wherein the secondmachine-learning model is a deep neural network model comprising asupport vector machine layer, and wherein the deep neural network modelis trained using a backpropagation algorithm.
 14. The system of claim12, wherein the machine-learning model comprises a neural networkcomprising at least one hidden layer, at least one neuron, and anactivation function, and wherein the neural network determines apredicted production rate at a predetermined time for the productionwell.
 15. The system of claim 12, wherein the machine-learning modelcomprises a neural network comprising at least one hidden layer, atleast one neuron, and an activation function, wherein the neural networkdetermines a predetermined amount of carbon dioxide emissions associatedwith providing electric power to the production well to achieve apredetermined production rate, and wherein the carbon dioxide injectionis based on the predetermined amount of carbon dioxide emissions. 16.The system of claim 12, wherein the machine-learning model comprises oneor more non-linear autoregressive neural network with an exogenousinput.
 17. The system of claim 12, wherein the reservoir data comprisesensor data from a plurality of sensors disposed in the injection well,and wherein the plurality of sensors are in a distributed chemicalsensor (DCS) network.
 18. The system of claim 12, wherein the reservoirdata comprise sensor data that describes gas content in the reservoirbased on one or more gas tracers.
 19. The system of claim 12, whereinthe carbon dioxide manager is configured to: determine, using a secondmachine-learning model, predicted carbon dioxide demand data for an sCO₂turbine for a predetermined period of time based on historicalelectric-power data.
 20. The system of claim 12, wherein the one or moresCO₂ turbines operate using carbon dioxide in a fluid state of carbondioxide (CO₂) that is held at or above a predetermined criticaltemperature and a predetermined critical pressure.